How to Transform Rows to Columns

Efficiently convert rows to columns in sql server

There are several ways that you can transform data from multiple rows into columns.

Using PIVOT

In SQL Server you can use the PIVOT function to transform the data from rows to columns:

select Firstname, Amount, PostalCode, LastName, AccountNumber
from
(
  select value, columnname
  from yourtable
) d
pivot
(
  max(value)
  for columnname in (Firstname, Amount, PostalCode, LastName, AccountNumber)
) piv;

See Demo.

Pivot with unknown number of columnnames

If you have an unknown number of columnnames that you want to transpose, then you can use dynamic SQL:

DECLARE @cols AS NVARCHAR(MAX),
    @query  AS NVARCHAR(MAX)

select @cols = STUFF((SELECT ',' + QUOTENAME(ColumnName) 
                    from yourtable
                    group by ColumnName, id
                    order by id
            FOR XML PATH(''), TYPE
            ).value('.', 'NVARCHAR(MAX)') 
        ,1,1,'')

set @query = N'SELECT ' + @cols + N' from 
             (
                select value, ColumnName
                from yourtable
            ) x
            pivot 
            (
                max(value)
                for ColumnName in (' + @cols + N')
            ) p '

exec sp_executesql @query;

See Demo.

Using an aggregate function

If you do not want to use the PIVOT function, then you can use an aggregate function with a CASE expression:

select
  max(case when columnname = 'FirstName' then value end) Firstname,
  max(case when columnname = 'Amount' then value end) Amount,
  max(case when columnname = 'PostalCode' then value end) PostalCode,
  max(case when columnname = 'LastName' then value end) LastName,
  max(case when columnname = 'AccountNumber' then value end) AccountNumber
from yourtable

See Demo.

Using multiple joins

This could also be completed using multiple joins, but you will need some column to associate each of the rows which you do not have in your sample data. But the basic syntax would be:

select fn.value as FirstName,
  a.value as Amount,
  pc.value as PostalCode,
  ln.value as LastName,
  an.value as AccountNumber
from yourtable fn
left join yourtable a
  on fn.somecol = a.somecol
  and a.columnname = 'Amount'
left join yourtable pc
  on fn.somecol = pc.somecol
  and pc.columnname = 'PostalCode'
left join yourtable ln
  on fn.somecol = ln.somecol
  and ln.columnname = 'LastName'
left join yourtable an
  on fn.somecol = an.somecol
  and an.columnname = 'AccountNumber'
where fn.columnname = 'Firstname'

Python Pandas: Convert Rows as Column headers

You're looking for pivot_table:

In [11]: medals = df.pivot_table('no of medals', ['Year', 'Country'], 'medal')

In [12]: medals
Out[12]:
medal             Bronze  Gold  Silver
Year Country
1896 Afghanistan       3     5       4
     Algeria           3     1       2

and if you want to reorder the columns:

In [12]: medals.reindex_axis(['Gold', 'Silver', 'Bronze'], axis=1)
Out[12]:
medal             Gold  Silver  Bronze
Year Country
1896 Afghanistan     5       4       3
     Algeria         1       2       3

How can I transform rows to columns?

use:

={"Date"\ "Type"\ "Metric"; INDEX(QUERY(SPLIT(
 FLATTEN(A2:A6&"×"&B1:L1&"×"&B2:L6); "×"); 
 "where Col3 is not null"; ))}

Convert rows to columns and group

Why not a conditional aggregation

SELECT Comedor      = com.NombrePuntoEntrega 
      ,APLICACIONES = SUM(case when a.NombreArea = 'APLICACIONES' then s.CantidadRaciones else 0 end) 
      ,ARANDANOS    = SUM(case when a.NombreArea = 'ARANDANOS'    then s.CantidadRaciones else 0 end) 
      ,PALTO        = SUM(case when a.NombreArea = 'PALTO'        then s.CantidadRaciones else 0 end) 
  FROM PPA_SolicitudRaciones c inner join  
       PPA_SolicitudRacionesDET S on c.IdSolicitud =s.IdSolicitud and s.IdLocalidad =c.IdLocalidad 
 INNER JOIN PPA_AREAS A ON A.IDLOCALIDAD=S.IDLOCALIDAD  and a.IdArea =s.IdArea 
 INNER JOIN PPA_PuntosEntrega com on com.IdLocalidad =s.IdLocalidad and com.IdPuntoEntrega =s.IdPuntoEntrega 
 WHERE (s.IdLocalidad =@IdLocalidad or @IdLocalidad =0)
    AND (s.IdArea =@IdArea or @IdArea =0)
    AND (c.FechaPedido between @FechaDel and @FechaAl   or   @FechaDel ='1900.01.01' )
    AND (c.IdTipoComida  =@TipoComida or @TipoComida=0)
 GROUP BY com.NombrePuntoEntrega

How to convert rows to columns in a Pandas groupby?

Use pivot to transform your dataframe:

# pivot args: 1st -> index, 2nd -> columns, 3rd -> values
>>> df.pivot(['sku_id', 'size', 'source'], 'timestamp', 'price') \
      .rename_axis(columns=None).add_prefix('price on ').reset_index()

   sku_id  size  source  price on 2021-01-10  price on 2021-07-10
0       1   6.0       1                115.0                  NaN
1       1   6.0       2                168.0                  NaN
2       1   9.0       1                152.0                152.0
3       2   8.0       1                149.0                  NaN

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